提出可解释的混合模型，实现眼底图像疾病检测的精准定位与决策透明
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
- 融合卷积与注意力机制，设计从结构上可解释的混合网络
- 单次前向传播生成特定类别稀疏证据图，准确率达最新水平
- 适合需要决策透明的医疗诊断场景，尤其关注可解释性需求
在许多医学影像任务中，卷积神经网络（CNN）能高效分层提取局部特征。近年来，视觉变压器（ViTs）凭借自注意力机制捕捉全局依赖关系而受到青睐，但缺乏卷积固有的空间定位能力。因此，结合CNN与ViT优点的混合模型被提出，但这些模型通常难以解释，限制了其在医学影像中的应用。本文提出一种可解释性设计的全卷积混合CNN-Transformer架构，用于眼底图像疾病检测。不同于广泛使用的后处理显著性方法，我们的方法直接生成忠实且局部化的证据图，真实反映模型决策过程。我们在两个基于彩色眼底图像的疾病检测任务上评估该方法，结果表明，相比黑箱模型和现有可解释模型，本模型实现了最先进的预测性能，并能在一次前向传播中生成类别特异性的稀疏证据图。代码已公开于：https://github.com/kdjoumessi/Self-Explainable-CNN-Transformer。
原文摘要 · Abstract (English)
In many medical imaging tasks, convolutional neural networks (CNNs) efficiently extract local features hierarchically. More recently, vision transformers (ViTs) have gained popularity, using self-attention mechanisms to capture global dependencies, but lacking the inherent spatial localization of convolutions. Therefore, hybrid models combining CNNs and ViTs have been developed to combine the strengths of both architectures. However, such hybrid models are difficult to interpret, which hinders their application in medical imaging. In this work, we introduce an interpretable-by-design hybrid fully convolutional CNN-Transformer architecture for retinal disease detection. Unlike widely used post-hoc saliency methods for ViTs, our approach generates faithful and localized evidence maps that directly reflect the mode's decision process. We evaluated our method on two medical tasks focused on disease detection using color fundus images. Our model achieves state-of-the-art predictive performance compared to black-box and interpretable models and provides class-specific sparse evidence maps in a single forward pass. The code is available at: https://github.com/kdjoumessi/Self-Explainable-CNN-Transformer.
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